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Empirical Method to Interpret Standard Deviation01:09

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The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Sequences are fundamental mathematical objects consisting of ordered lists of numbers that follow a specific rule or pattern. Sequences are critical in various mathematical concepts, including calculus, series, and number theory. They can model real-world phenomena such as population growth, financial investments, and physical processes like the diminishing height of a bouncing ball.Each number in a sequence is referred to as a term. Typically, the terms are denoted as a1, a2, a3,…, where...
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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Sequence clustering in bioinformatics: an empirical study.

Quan Zou1,2, Gang Lin1, Xingpeng Jiang3

  • 1Tianjin University.

Briefings in Bioinformatics
|September 22, 2018
PubMed
Summary
This summary is machine-generated.

Sequence clustering is crucial for analyzing big sequencing data in metagenomics. This review compares popular tools to help users select stable, quick, and accurate methods for microbiome analysis.

Keywords:
16S ribosomal RNAmicrobiomeoperational taxonomic unitsequence clusteringsequence redundancy removal

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Metagenomics and microbiomics generate massive DNA/RNA sequence data.
  • Efficient and accurate sequence clustering is essential for data analysis.
  • Existing tools may yield diverse results, posing challenges for users.

Purpose of the Study:

  • To review and compare popular sequence clustering tools.
  • To explain the underlying computational principles of these tools.
  • To guide bioinformatics users in selecting appropriate tools for big sequencing data analysis.

Main Methods:

  • Selection of several popular sequence clustering software tools.
  • Explanation of the core computational principles for each tool.
  • Comparative analysis of tools using two independent benchmark datasets.

Main Results:

  • Characterization of selected clustering tools.
  • Performance comparison based on benchmark datasets.
  • Identification of strengths and weaknesses of different clustering approaches.

Conclusions:

  • Understanding clustering mechanisms aids in interpreting results.
  • Effective tool selection is key for analyzing large-scale sequencing data.
  • This review provides a practical guide for bioinformatics users.